Date: Tue May 28 14:39:12 2019
Scientist: Ran Yin
Sequencing (Waksman): Dibyendu Kumar
Statistics: Davit Sargsyan
Principal Investigator: Ah-Ng Kong

Meta data

# sink(file = "tmp/log_nrf2ubiome_data_visualization_may2019_v1.Rmd.txt")
# date()
options(scipen=999)
require(knitr)
Loading required package: knitr
require(kableExtra)
Loading required package: kableExtra
# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))
options(stringsAsFactors = FALSE)
# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE, 
#                       message = FALSE,
#                       warning = FALSE,
#                       error = FALSE)
# On Windows set multithread=FALSE----
mt <- TRUE
require(data.table)
Loading required package: data.table
data.table 1.12.2 using 18 threads (see ?getDTthreads).  Latest news: r-datatable.com
require(phyloseq)
Loading required package: phyloseq
require(ggplot2)
Loading required package: ggplot2
require(plotly)
Loading required package: plotly

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
# require(data.table)
require(DT)
Loading required package: DT
require(shiny)
Loading required package: shiny

Attaching package: ‘shiny’

The following objects are masked from ‘package:DT’:

    dataTableOutput, renderDataTable
source("source/functions_may2019.R")
# Load data----
# Counts
load("data_may2019/ps_may2019.RData")
# Taxonomy
load("data_may2019/taxa.RData")
taxa <- data.table(seq16s = rownames(taxa),
                   taxa)
# Samples
samples <- ps_may2019@sam_data
DT::datatable(samples,
              options = list(pageLength = nrow(samples)))

Taxonomic Ranks:

King Phillip Can nOt Find Green Socks * Kingdom
* Phylum
* Class
* Order
* Family
* Genus
* Species

Prune data

Check mapping.


 Bacteria Eukaryota      <NA> 
     7994       116        19 

     Acidobacteria     Actinobacteria      Bacteroidetes      Cyanobacteria 
                 3                177               2102                 31 
   Deferribacteres Epsilonbacteraeota         Firmicutes       Fusobacteria 
                10                 27               4997                  1 
       Parabasalia    Patescibacteria     Planctomycetes     Proteobacteria 
                 1                 63                  1                202 
       Tenericutes    Verrucomicrobia               <NA> 
                84                 66                364 

First, all OTUs that were not bacteria were removed. Also, OTUs that were not mapped to a phylum were removed as these are usually sequencing artifacts.


Bacteria 
    7764 

     Acidobacteria     Actinobacteria      Bacteroidetes      Cyanobacteria 
                 3                177               2102                 31 
   Deferribacteres Epsilonbacteraeota         Firmicutes       Fusobacteria 
                10                 27               4997                  1 
   Patescibacteria     Planctomycetes     Proteobacteria        Tenericutes 
                63                  1                202                 84 
   Verrucomicrobia 
                66 

Richness (Alpha diversity)

OTU table

Total counts per sample (i.e. sequencing depth)

Counts at Phylum level

Relative abundance (%) at Phylum level

Remove phyla with relative abundance of >= 1% in less than 10% of samples (i.e. prevalence >= 10%).

Hence, only 6 out of 13 Phyla were studied in this analysis: Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Tenericutes and Verrucomicrobia. Relative abundance at the next taxonomic level (Class) was, therefore, computed relative to the sum of these 6 Phyla.

[1] "Actinobacteria"  "Bacteroidetes"   "Firmicutes"      "Proteobacteria" 
[5] "Tenericutes"     "Verrucomicrobia"

7,628 OTUs, down from 7,764 OTUs in the previous table.

Relative Abundance in Samples at Different Taxonomic Ranks

1. Class

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Treatment)) +
  facet_wrap(~ Class,
             scale = "free_y") +
  geom_line() +
  geom_point(aes(shape = Treatment,
                 color = Treatment),
             size = 5,
             alpha = 0.5)
print(p0)

p1 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")
ggplotly(p1)

p2 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_log10(" Log10 Scale of Relative Abundance (%)")
ggplotly(p2)
Transformation introduced infinite values in continuous x-axis

2. Order

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Treatment)) +
  facet_wrap(~ Order,
             scale = "free_y") +
  geom_line() +
  geom_point(aes(shape = Treatment,
                 color = Treatment),
             size = 5,
             alpha = 0.5)
print(p0)

p1 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")
ggplotly(p1)

p2 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_log10(" Log10 Scale of Relative Abundance (%)")
ggplotly(p2)
Transformation introduced infinite values in continuous x-axis

Session Information

sessionInfo()
R version 3.5.0 (2018-04-23)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux Server 7.5 (Maipo)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] shiny_1.3.2       DT_0.6            plotly_4.9.0      ggplot2_3.1.1    
[5] phyloseq_1.26.1   data.table_1.12.2 kableExtra_1.1.0  knitr_1.23       

loaded via a namespace (and not attached):
 [1] Biobase_2.42.0      httr_1.4.0          tidyr_0.8.3         jsonlite_1.6       
 [5] viridisLite_0.3.0   splines_3.5.0       foreach_1.4.4       assertthat_0.2.1   
 [9] stats4_3.5.0        yaml_2.2.0          pillar_1.4.0        lattice_0.20-35    
[13] glue_1.3.1          digest_0.6.19       promises_1.0.1      XVector_0.22.0     
[17] rvest_0.3.4         colorspace_1.4-1    httpuv_1.5.1        htmltools_0.3.6    
[21] Matrix_1.2-14       plyr_1.8.4          pkgconfig_2.0.2     zlibbioc_1.28.0    
[25] xtable_1.8-4        purrr_0.3.2         scales_1.0.0        webshot_0.5.1      
[29] later_0.8.0         tibble_2.1.1        mgcv_1.8-23         IRanges_2.16.0     
[33] withr_2.1.2         BiocGenerics_0.28.0 lazyeval_0.2.2      mime_0.6           
[37] survival_2.41-3     magrittr_1.5        crayon_1.3.4        evaluate_0.13      
[41] nlme_3.1-137        MASS_7.3-49         xml2_1.2.0          vegan_2.5-5        
[45] tools_3.5.0         hms_0.4.2           stringr_1.4.0       Rhdf5lib_1.4.3     
[49] S4Vectors_0.20.1    munsell_0.5.0       cluster_2.0.7-1     Biostrings_2.50.2  
[53] ade4_1.7-13         compiler_3.5.0      rlang_0.3.4         rhdf5_2.26.2       
[57] grid_3.5.0          iterators_1.0.10    biomformat_1.10.1   rstudioapi_0.10    
[61] htmlwidgets_1.3     crosstalk_1.0.0     igraph_1.2.4.1      labeling_0.3       
[65] rmarkdown_1.12      gtable_0.3.0        codetools_0.2-15    multtest_2.38.0    
[69] reshape2_1.4.3      R6_2.4.0            dplyr_0.8.1         permute_0.9-5      
[73] readr_1.3.1         ape_5.3             stringi_1.4.3       parallel_3.5.0     
[77] Rcpp_1.0.1          tidyselect_0.2.5    xfun_0.7           
---
title: "Nrf2 BL6 PEITC 16S Microbiome Data Visualization"
output: 
  html_notebook:
    toc: yes
    toc_float: yes
---
Date: `r date()`     
Scientist: [Ran Yin](mailto:ry147@scarletmail.rutgers.edu)      
Sequencing (Waksman): [Dibyendu Kumar](mailto:dk@waksman.rutgers.edu)      
Statistics: [Davit Sargsyan](mailto:sargdavid@gmail.com)      
Principal Investigator: [Ah-Ng Kong](mailto:kongt@pharmacy.rutgers.edu) 

# Meta data
```{r header}
# sink(file = "tmp/log_nrf2ubiome_data_visualization_may2019_v1.Rmd.txt")
# date()
options(scipen=999)

require(knitr)
require(kableExtra)

# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))
options(stringsAsFactors = FALSE)
# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE, 
#                       message = FALSE,
#                       warning = FALSE,
#                       error = FALSE)

# On Windows set multithread=FALSE----
mt <- TRUE

require(data.table)
require(phyloseq)
require(ggplot2)
require(plotly)
# require(data.table)
require(DT)
require(shiny)
source("source/functions_may2019.R")

# Load data----
# Counts
load("data_may2019/ps_may2019.RData")

# Taxonomy
load("data_may2019/taxa.RData")
taxa <- data.table(seq16s = rownames(taxa),
                   taxa)

# Samples
samples <- ps_may2019@sam_data
DT::datatable(samples,
              options = list(pageLength = nrow(samples)))
```

# Taxonomic Ranks:
**K**ing **P**hillip **C**an n**O**t **F**ind **G**reen **S**ocks
* Kingdom                
* Phylum                    
* Class                   
* Order                   
* Family     
* Genus     
* Species  

# Prune data
Check mapping.
```{r check_mapping, warning=FALSE,echo=FALSE,message=FALSE}
table(tax_table(ps_may2019)[, "Kingdom"], 
      exclude = NULL)
table(tax_table(ps_may2019)[, "Phylum"], 
      exclude = NULL)
```

First, all OTUs that were not bacteria were removed. Also, OTUs that were not mapped to a phylum were removed as these are  usually sequencing artifacts.
```{r prune, warning=FALSE,echo=FALSE,message=FALSE}
# 1. Keep bacteria only (i.e. remove archea and eucaryota)
# 2. Remove if phylum is unmapped
ps0 <- subset_taxa(ps_may2019, 
                   Kingdom == "Bacteria" &
                     !is.na(Phylum))

table(tax_table(ps0)[, "Kingdom"], 
      exclude = NULL)
table(tax_table(ps0)[, "Phylum"], 
      exclude = NULL)
```

# Richness (Alpha diversity)
```{r richness, warning=FALSE,echo=FALSE,message=FALSE,fig.width=10,fig.height=5}
ps0@sam_data$Diet_Week <- paste(samples$TREATMENT,
                           samples$WEEK,
                           sep = "_")
ps0@sam_data$ID <- substr(x = ps0@sam_data$SAMPLE_NAME,
                                 start = 2,
                                 stop = 3)
p1 <- plot_richness(ps0,
                    x = "Diet_Week", 
                    measures = "Shannon",
                    color = "ID") +
  geom_line(aes(group = ID),
            color = "black") +
  geom_point(shape = 21,
             size = 3,
             color = "black")
ggplotly(p1)
```

# OTU table
```{r otu_table, warning=FALSE,echo=FALSE,message=FALSE}
otu <- data.table(ps0@tax_table@.Data,
                  t(ps0@otu_table@.Data))
DT::datatable(otu)
```

# Total counts per sample (i.e. sequencing depth)
```{r Tax, warning=FALSE,echo=FALSE,message=FALSE,fig.width=10,fig.height=5}
t1 <- colSums(otu[, 7:ncol(otu)])
t1 <- data.table(SAMPLE_NAME = names(t1),
                 Total = t1)

tmp <- data.table(SAMPLE_NAME = samples$SAMPLE_NAME,
                  TREATMENT = samples$TREATMENT,
                  WEEK = samples$WEEK)

t1 <- merge(tmp,
            t1,
            by = "SAMPLE_NAME")

p1 <- ggplot(t1,
             aes(x = SAMPLE_NAME,
                 y = Total,
                 fill = TREATMENT,
                 colour = WEEK)) +
  geom_bar(stat = "identity") +
  scale_x_discrete("Sample Name") +
  scale_y_continuous("Number of Reads") +
  scale_fill_discrete("Group") +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1)) 
ggplotly(p1)
```

# Counts at Phylum level
```{r counts_p, warning=FALSE,echo=FALSE,message=FALSE}
counts_p <- counts_by_tax_rank(dt1 = otu,
                               aggr_by = "Phylum")
DT::datatable(counts_p,
              options = list(pageLength = nrow(counts_p)))
```

# Relative abundance (%) at Phylum level
```{r ra_p, warning=FALSE,echo=FALSE,message=FALSE}
ra_p <- ra_by_tax_rank(counts = counts_p)
DT::datatable(ra_p,
              options = list(pageLength = nrow(ra_p)))
```

Remove phyla with relative abundance of >= 1% in less than 10% of samples (i.e. prevalence >= 10%).

```{r prev_p, warning=FALSE,echo=FALSE,message=FALSE}
t1 <- data.table(Phylum = ra_p$Phylum,
                 Prevalence = round(100*rowSums(ra_p[, 2:ncol(ra_p)] >= 1)/30, 1))
DT::datatable(t1,
              options = list(pageLength = nrow(ra_p)))
```

Hence, only 6 out of 13 Phyla were studied in this analysis: Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, Tenericutes and Verrucomicrobia. Relative abundance at the next taxonomic level (Class) was, therefore, computed relative to the sum of these 6 Phyla.

```{r keep_6_phyla, warning=FALSE,echo=FALSE,message=FALSE}
keep_p <- t1$Phylum[t1$Prevalence >= 10]
keep_p 
ps1 <- subset_taxa(ps0, 
                   Phylum %in% keep_p )
otu1 <- data.table(ps1@tax_table@.Data,
                   t(ps1@otu_table@.Data))
DT::datatable(otu1)
```

7,628 OTUs, down from 7,764 OTUs in the previous table.


# Relative Abundance in Samples at Different Taxonomic Ranks
## 1. Class
```{r counts_c, warning=FALSE,echo=FALSE,message=FALSE,fig.width=10,fig.height=6}
counts_c <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Class")
ra_c <- ra_by_tax_rank(counts_c)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Class")])

ra_c <- merge(tax.ranks,
              ra_c,
              by = "Class")

total <- rowSums(ra_c[, 3:ncol(ra_c)])

ra_c$Class <- factor(ra_c$Class,
                     levels = ra_c$Class[order(total)])

ra_c$Phylum <- factor(ra_c$Phylum,
                      levels = unique(ra_c$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_c,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_c),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = samples$SAMPLE_NAME,
                        WEEK = samples$WEEK,
                        TREATMENT = samples$TREATMENT),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Class,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1)
```

```{r means_c, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_c,
               samples = samples,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Class = lra$Class),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Class"]
ul <- unique(mu[, c("Class", 
                    "total")])
ul <- ul[order(total),]
mu$Class <- factor(mu$Class,
                   level = ul$Class)
DT::datatable(mu)
```


```{r means_c_p0, fig.width = 10, fig.height = 5}
p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Treatment)) +
  facet_wrap(~ Class,
             scale = "free_y") +
  geom_line() +
  geom_point(aes(shape = Treatment,
                 color = Treatment),
             size = 5,
             alpha = 0.5)
print(p0)
```


```{r means_c_p1, fig.width = 10, fig.height = 10}
p1 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")
ggplotly(p1)

p2 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_log10(" Log10 Scale of Relative Abundance (%)")

ggplotly(p2)
```

## 2. Order
```{r counts_o, warning=FALSE,echo=FALSE,message=FALSE,fig.width=10,fig.height=6}
counts_o <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Order")
ra_o <- ra_by_tax_rank(counts_o)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Order")])

ra_o <- merge(tax.ranks,
              ra_o,
              by = "Order")

total <- rowSums(ra_o[, 3:ncol(ra_o)])

ra_o$Order <- factor(ra_o$Order,
                     levels = ra_o$Order[order(total)])

ra_o$Phylum <- factor(ra_o$Phylum,
                      levels = unique(ra_o$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_o,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_o),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = samples$SAMPLE_NAME,
                        WEEK = samples$WEEK,
                        TREATMENT = samples$TREATMENT),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Order,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1)
```
```{r means_o, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_o,
               samples = samples,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Order = lra$Order),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Order"]
ul <- unique(mu[, c("Order", 
                    "total")])
ul <- ul[order(total),]
mu$Order <- factor(mu$Order,
                   level = ul$Order)
DT::datatable(mu)
```

```{r means_o_p0, fig.width = 10, fig.height = 5}
p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Treatment)) +
  facet_wrap(~ Order,
             scale = "free_y") +
  geom_line() +
  geom_point(aes(shape = Treatment,
                 color = Treatment),
             size = 5,
             alpha = 0.5)
print(p0)
```
```{r means_o_p1, fig.width = 10, fig.height = 10}
p1 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")
ggplotly(p1)

p2 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_log10(" Log10 Scale of Relative Abundance (%)")

ggplotly(p2)
```

# Session Information
```{r info,eval=TRUE}
sessionInfo()
```